CN111639034A - Test method, device, equipment and computer storage medium - Google Patents
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Abstract
The invention relates to the technical field of financial technology (Fintech) and discloses a testing method, which comprises the following steps: determining a data input source according to the input service scene type, and acquiring a first preset number of target label information in untrained label information of a knowledge graph according to the data input source; obtaining evaluation indexes corresponding to the target label information, and obtaining index influence factors corresponding to the evaluation indexes; calculating a result weight value corresponding to each target label information according to each evaluation index and each index influence factor, and correcting a recommendation score value corresponding to each target label information according to each result weight value; and determining a second preset number of target recommendation score values based on the corrected recommendation score values, and testing the scheme to be evaluated corresponding to each target recommendation score value as a test case. The invention also discloses a test device, equipment and a computer storage medium. The invention improves the effectiveness of automatic testing.
Description
Technical Field
The invention relates to the technical field of testing of financial technology (Fintech), in particular to a testing method, a testing device, testing equipment and a computer storage medium.
Background
With the development of computer technology, more and more technologies (big data, distributed, artificial intelligence, etc.) are applied to the financial field, and the traditional financial industry is gradually changing to financial technology (Fintech), but because of the requirements of security and real-time performance of the financial industry, higher requirements are also put forward on the technologies. At present, when a tester carries out various automatic tests (such as case regression tests), the tester generally executes test cases manually according to own experience, and is easily influenced by subjective consciousness of the tester, so that the problems of dispersed test concerns, no need of pertinence, easy neglect of key points, insufficient coverage of key business scenes and the like exist, and the test effectiveness is low. Therefore, how to improve the effectiveness of the automated testing becomes a technical problem to be solved urgently at present.
Disclosure of Invention
The invention mainly aims to provide a testing method, a testing device, testing equipment and a computer storage medium, and aims to solve the technical problem of how to improve the effectiveness of automatic testing.
In order to achieve the above object, the present invention provides a testing method, comprising the steps of:
determining a data input source according to an input service scene type, and acquiring a first preset number of target label information in untrained label information of a knowledge graph according to the data input source;
obtaining evaluation indexes corresponding to the target label information, and obtaining index influence factors corresponding to the evaluation indexes;
calculating a result weight value corresponding to each target label information according to each evaluation index and each index influence factor, and correcting a recommendation score value corresponding to each target label information according to each result weight value;
and determining a second preset number of target recommendation score values based on the corrected recommendation score values, and testing the scheme to be evaluated corresponding to the target recommendation score values as a test case.
Optionally, the step of calculating a result weight value corresponding to each target tag information according to each evaluation indicator and each indicator influence factor includes:
calculating a total value of a correction index of the scheme to be evaluated corresponding to each target label information according to each evaluation index and each index influence factor;
and calculating a result weight value corresponding to each target label information according to each correction index total value and each evaluation index.
Optionally, the step of correcting the recommended score value of the to-be-evaluated scheme corresponding to each target tag information according to each result weight value includes:
acquiring recommendation score values of the to-be-evaluated scheme corresponding to the target label information, sequentially traversing the recommendation score values, and determining a current result weight value corresponding to the currently traversed current recommendation score value in the result weight values;
and calculating the current recommendation score value and the current result weight value based on a preset calculation formula, and correcting the current recommendation score value according to a calculation result until all the recommendation score values are traversed.
Optionally, the step of obtaining the recommended score value of the to-be-evaluated scheme corresponding to each piece of target tag information includes:
and traversing the schemes to be evaluated corresponding to the target label information in sequence, determining a target library of the currently traversed scheme to be evaluated in the knowledge graph, acquiring a default score value of the target library, and taking the default score value as a recommended score value of the current scheme to be evaluated until the traversal of each scheme to be evaluated is completed.
Optionally, the step of obtaining an evaluation index corresponding to each piece of target tag information includes:
and traversing all the target label information in sequence, determining a plurality of different types of index quantity values corresponding to the currently traversed current target label information, and taking all the index quantity values as evaluation indexes corresponding to the current target label information until all the target label information is traversed.
Optionally, the step of obtaining the index influence factor corresponding to each evaluation index includes:
and traversing each evaluation index in sequence, determining all index quantity values in the currently traversed evaluation index, and determining an index influence factor corresponding to the currently evaluated index according to each index quantity value until each evaluation index is traversed.
Optionally, the step of obtaining a first preset number of target label information from untrained label information of a knowledge graph according to the data input source includes:
acquiring a plurality of untrained label information in a knowledge graph, and sequencing the untrained label information according to the data input source;
and acquiring a first preset number of target label information in each untrained label information according to the sequencing result of the sequencing.
In addition, to achieve the above object, the present invention also provides a test apparatus, comprising:
the system comprises a determining module, a judging module and a judging module, wherein the determining module is used for determining a data input source according to an input service scene type and acquiring a first preset amount of target label information in untrained label information of a knowledge graph according to the data input source;
the acquisition module is used for acquiring evaluation indexes corresponding to the target label information and acquiring index influence factors corresponding to the evaluation indexes;
the correction module is used for calculating a result weight value corresponding to each target label information according to each evaluation index and each index influence factor and correcting a recommendation score value corresponding to each target label information according to each result weight value;
and the test module is used for determining a second preset number of target recommendation score values based on the corrected recommendation score values and testing the scheme to be evaluated corresponding to the target recommendation score values as a test case.
In addition, to achieve the above object, the present invention also provides a test apparatus, including: a memory, a processor and a test program stored on the memory and executable on the processor, the test program, when executed by the processor, implementing the steps of the test method as described above.
In addition, to achieve the above object, the present invention further provides a computer storage medium having a test program stored thereon, wherein the test program realizes the steps of the test method as described above when executed by a processor.
Determining a data input source according to an input service scene type, and acquiring a first preset number of target label information in untrained label information of a knowledge graph according to the data input source; obtaining evaluation indexes corresponding to the target label information, and obtaining index influence factors corresponding to the evaluation indexes; calculating a result weight value corresponding to each target label information according to each evaluation index and each index influence factor, and correcting a recommendation score value corresponding to each target label information according to each result weight value; and determining a second preset number of target recommendation score values based on the corrected recommendation score values, and testing the scheme to be evaluated corresponding to the target recommendation score values as a test case. The data input source is determined according to the service scene type, the target label information is obtained from the knowledge graph, the result weight value is calculated according to the evaluation index and the index influence factor corresponding to the target label information, the recommendation score value corresponding to the target label information is corrected, and the test case is determined according to the corrected recommendation score value to be tested, so that the test case which accords with the service scene type can be effectively selected to be tested, the phenomenon that the test case is manually selected to be tested by self in the prior art to cause the reduction of the test effectiveness is avoided, and the effectiveness and the accuracy of the automatic test are improved.
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FIG. 1 is a schematic diagram of a test apparatus in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a first embodiment of the testing method of the present invention;
FIG. 3 is a schematic diagram of an apparatus module of the testing apparatus of the present invention;
FIG. 4 is a schematic illustration of the recommendation score of a knowledge graph in a test method of the present invention;
FIG. 5 is a schematic view of a testing process in the testing method of the present invention;
FIG. 6 is a schematic diagram of the construction of a knowledge graph in the testing method of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
As shown in fig. 1, fig. 1 is a schematic structural diagram of a test device of a hardware operating environment according to an embodiment of the present invention.
The test equipment of the embodiment of the invention can be a PC (personal computer) or server equipment, and a Java virtual machine runs on the test equipment.
As shown in fig. 1, the test apparatus may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein a communication bus 1002 is used to enable connective communication between these components. The user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a non-volatile memory (e.g., a magnetic disk memory). The memory 1005 may alternatively be a storage device separate from the processor 1001.
Those skilled in the art will appreciate that the test device configuration shown in FIG. 1 does not constitute a limitation of the device and may include more or fewer components than those shown, or some components may be combined, or a different arrangement of components.
As shown in fig. 1, a memory 1005, which is a kind of computer storage medium, may include therein an operating system, a network communication module, a user interface module, and a test program.
In the test device shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call a test program stored in the memory 1005 and perform operations in the test method described below.
Based on the hardware structure, the embodiment of the test method is provided.
Referring to fig. 2, fig. 2 is a schematic flow chart of a first embodiment of the testing method of the present invention, the method includes:
step S10, determining a data input source according to the input service scene type, and acquiring a first preset number of target label information in untrained label information of the knowledge graph according to the data input source;
in the present embodiment, version management at the time of test is performed by using a GIT (distributed version control system) and is integrated into a JENKINS (open source software project). And then, calling a CI (Continuous Integration) interface of a preset quality middle platform (which can be represented by TctpTest) to perform version automatic deployment, namely, determining a service scene type according to information input by developers or testers, and requesting a knowledge graph to obtain case label information recommended under the service scene type. After feedback information fed back by the knowledge graph is obtained, all test cases to be tested are determined according to the feedback information, and then all test cases are directly executed through the test execution engine. And after the test execution engine finishes executing, the corresponding execution result is output and stored in the database, so that the TctpTest platform can trace back all the time through the data report with the execution result in the database, the test efficiency is higher, and the missing rate can be reduced.
Wherein the knowledge-graph comprises three functions. The first part is to generate an efficient and valuable benchmark label case base through multi-dimensional modeling. The second part is to interface with the TctpTest platform, and the interface may include: requesting recommended tags, querying more tags (adopting paging and DB (database) in-table query fields to create indexes, avoiding excessive tag information, reducing bandwidth usage, improving query performance, supporting fuzzy matching, finding tag information wanted by an executor as soon as possible), feeding back tag information (a push interface), executing a specified tag test case and the like. And the third part is that the executed label case information can be used as a new data input source to participate in the modeling algorithm again to obtain more accurate label weight.
Therefore, in this embodiment, the input service scenario type may be obtained first, and the parent function type and the child function type associated with this service scenario type are obtained in the database to form the data input source. That is, the data input sources include a service scenario type, a parent function type, and a child function type. Where the parent function type may be a large requirement for the test case. While the sub-level function type may be a small demand under a large demand. For example, the account opening account is a parent function type, and the card information cardinfo (card information table) under the account opening may be a child function type.
Therefore, when the data input source is obtained, M times of training data (namely a certain amount of untrained training data) with the flag of 0 can be obtained from the knowledge graph according to the data input source, training case label information (namely untrained label information) under the service scene type is determined according to the training data, then the untrained label information is ranked by using the use times of the parent function type and the child function type in the data input source as keywords, and the untrained label information corresponding to the maximum value of the top N (namely the first preset amount) of the use times is selected from the ranking results and is used as the target label information. That is, it is determined that score (recommended score value) values corresponding to the target tag information need to be modified.
Step S20, obtaining evaluation indexes corresponding to the target label information, and obtaining index influence factors corresponding to the evaluation indexes;
after the information of each target tag is obtained, the cases corresponding to the information of the target tags can be used as schemes to be evaluated, and ranking times of actual use times of the TctpTest platform are used as evaluation indexes according to the library, the service scene and the test expert experience score of the cases of the schemes to be evaluated, namely:
And in the present embodiment, an index influence factor is given to the degree of importance occupied by each evaluation index, that is:
And the knowledge graph comprises a knowledge public base, a BUG experience base, a specific service scene base and a training database. As shown in fig. 4, the knowledge base includes the most basic use case, and the main flow is generally defaulted to be executed in this business scenario, so the score (recommended score value) of the knowledge base may be set to the maximum value MAX. Since the BUG experience base is only performed in a very specific scenario, the score of the BUG experience base can be set to the median MID. And a specific service scenario library is only specific to some specific service scenarios, so the score of the specific service scenario library can be set to the lowest value MIN. And the training database only influences the score values of the BUG experience library and the specific service scene library, but does not influence the score values of the knowledge public library. In the embodiment, the recommendation of the tag use case executed each time is dependent on the size of score, and the greater the score is, the better the recommendation is. Therefore, when calculating the evaluation index, it is necessary to detect a library in which a case of a solution to be evaluated is located, so as to determine the evaluation index based on score.
Step S30, calculating result weight values corresponding to the target label information according to the evaluation indexes and the index influence factors, and correcting recommendation score values corresponding to the target label information according to the result weight values;
after the evaluation indexes and the index influence factors are obtained, the result weight value corresponding to each target label information can be calculated, and the recommended score value of the to-be-evaluated scheme corresponding to each target label information is corrected according to each result weight value. It should be noted that one result weight value only corrects the recommendation score value of the to-be-evaluated scheme associated with the result weight value, and does not change other recommendation score values.
Namely, a new matrix is obtained:
wherein, ciExpressed as the corrected total index value of the ith test case, and calculating a formula of the result weight value in the ith test case:
when the respective result weight values are acquired and the recommendation score value is modified according to the result weight values, the corrected recommendation score value may be determined according to the formula score (1+ p). Where score is the recommendation score value and the corrected recommendation score value cannot exceed the default maximum score value in the knowledge-graph. And after correcting the recommended score value, setting the training data, flag, of the target label information corresponding to the corrected recommended score value to 1 (namely, determining that the processing is performed).
Step S40, determining a second preset number of target recommendation score values based on the corrected recommendation score values, and testing a to-be-evaluated scheme corresponding to each target recommendation score value as a test case.
After the corrected recommendation score values are obtained, the corrected recommendation score values can be sorted according to the sequence from big to small, a second preset number of recommendation score values (namely target recommendation score values) with the recommendation score values larger than a certain value are selected from the recommendation score values, the schemes to be evaluated corresponding to the second preset number of recommendation score values are used as test cases to be placed in a test execution engine for testing, and after the testing is finished, the test results are actively stored in a database.
In addition, in order to assist understanding of the test flow in this embodiment, the following description is given by way of example.
For example, as shown in fig. 5, a developer or a tester determines a service scene type in a Tctp Test platform, and requests a recommendation tag from a knowledge graph according to the service scene type, and the knowledge graph performs modeling to screen out a suitable recommendation tag and returns the recommendation tag to the Tctp Test platform. And the Tctp Test platform returns the case corresponding to the recommended label to be executed in the Test execution engine according to the knowledge graph, namely the Test execution engine requests to execute the corresponding case (namely the Test case), and simultaneously the Tctp Test platform feeds back information (namely the case execution condition) to the knowledge graph. After the test case is executed, the test execution engine outputs the result and stores the result in a Database (DB) so as to call the knowledge graph.
The architecture composition of the knowledge graph can be shown in fig. 6, and includes a knowledge public base, a BUG experience base, a service scene base and a training database. The business scenario library comprises a scenario type, a parent function type, a child function type and SCORE values, and modeling in the knowledge graph is performed based on the SCORE values of the business scenario library, namely N pieces with the highest practical utilization degrees are selected from near M training trees (about M100 test cases), and the utilization rates of different intervals correspond to different SCORE weights. And when modeling is carried out in the knowledge graph, data input sources (comprising type, parent type and child type) are obtained from the training database for modeling. And the knowledge public base comprises the most basic use cases, such as account opening, login and the like. The BUG experience library is a case library which is found to be representative BUG in production and summarizes output of sediment. The service scenario library is a test case collection library aiming at scenarios involved in specific projects. The training database is a repository of use case label information which is finally determined each time by an executor on a Tctp Test platform. And the knowledge public library, the BUG experience library and the service scene library are case library sets directly facing and provided for test executors, and can be regularly maintained and updated by test personnel. And the training database is a bottom engine library and is used for inputting information of the executing label through actual test, modeling and training, and feeding back the obtained test guide label to the other three libraries to form a closed loop of the knowledge graph. And the knowledge fusion can be carried out in the knowledge map, namely, the repeated cases can be automatically identified and eliminated aiming at case failure marks. And moreover, the knowledge can be stored in the knowledge graph, namely, a key index of the service scene type + the parent function type can be established so as to accelerate the query.
In the embodiment, a data input source is determined according to an input service scene type, and a first preset amount of target label information is acquired from untrained label information of a knowledge graph according to the data input source; obtaining evaluation indexes corresponding to the target label information, and obtaining index influence factors corresponding to the evaluation indexes; calculating a result weight value corresponding to each target label information according to each evaluation index and each index influence factor, and correcting a recommendation score value corresponding to each target label information according to each result weight value; and determining a second preset number of target recommendation score values based on the corrected recommendation score values, and testing the scheme to be evaluated corresponding to the target recommendation score values as a test case. The data input source is determined according to the service scene type, the target label information is obtained from the knowledge graph, the result weight value is calculated according to the evaluation index and the index influence factor corresponding to the target label information, the recommendation score value corresponding to the target label information is corrected, and the test case is determined according to the corrected recommendation score value to be tested, so that the test case which accords with the service scene type can be effectively selected to be tested, the phenomenon that the test case is manually selected to be tested by self in the prior art to cause the reduction of the test effectiveness is avoided, and the effectiveness and the accuracy of the automatic test are improved.
Further, a second embodiment of the testing method of the present invention is proposed based on the first embodiment of the testing method of the present invention. This embodiment is a step S30 of the first embodiment of the present invention, and a refinement of the step of calculating a result weight value corresponding to each piece of target label information according to each of the evaluation indicators and each of the indicator influence factors includes:
step a, calculating a total value of a correction index of a scheme to be evaluated corresponding to each target label information according to each evaluation index and each index influence factor;
in this embodiment, after the evaluation indexes and the index influence factors corresponding to the evaluation indexes are obtained, the total value of the correction indexes of the to-be-evaluated scheme corresponding to the target label information may be calculated through a calculation formula set in advance. The calculation formula can be:
wherein, ciExpressed as the corrected index total value (i.e. the corrected index total value) of the ith test case.
And b, calculating a result weight value corresponding to each target label information according to each correction index total value and each evaluation index.
After the total value of the correction index of each scheme to be evaluated is obtained, the result weight value corresponding to each target label information can be calculated in sequence according to each total value of the correction index and each evaluation index. The calculation formula can be:
in this embodiment, the accuracy of the calculated result weight value is ensured by calculating the total value of the correction index according to each evaluation index and each index influence factor and calculating the result weight value according to the total value of the correction index.
Further, the step of correcting the recommended score value of the to-be-evaluated scheme corresponding to each target tag information according to each result weight value includes:
step c, acquiring recommendation score values of the to-be-evaluated scheme corresponding to the target label information, sequentially traversing the recommendation score values, and determining a current result weight value corresponding to the currently traversed current recommendation score value in the result weight values;
in this embodiment, after obtaining each result weight value, it is further required to obtain a recommendation score value of the to-be-evaluated scheme corresponding to each target tag information in the knowledge graph, sequentially traverse each recommendation score value, determine a currently traversed current recommendation score value, and then obtain a result weight value (i.e., a current result weight value) corresponding to the current recommendation score value in each result weight value.
And d, calculating the current recommendation score value and the current result weight value based on a preset calculation formula, and correcting the current recommendation score value according to a calculation result until all the recommendation score values are traversed.
After the current recommendation score value and the current result weight value are obtained, the current recommendation score value can be corrected according to a preset calculation formula. The preset calculation formula may be score (1+ p), score is the current recommended score value, and p is the current result weight value. It should be noted that each recommendation score value is corrected in the same manner until the traversal of each recommendation score value is completed.
In this embodiment, the current result weight value is determined in each result weight value according to the current recommendation score value, calculation is performed according to the current recommendation score value and the current result weight value, and the current recommendation score value is corrected based on the calculation result, so that the accuracy of correcting the current recommendation score value is guaranteed.
Specifically, the step of obtaining the recommended score value of the to-be-evaluated scheme corresponding to each piece of target tag information includes:
and e, sequentially traversing the schemes to be evaluated corresponding to the target label information, determining a target library of the currently traversed current schemes to be evaluated in the knowledge graph, acquiring a default score value of the target library, and taking the default score value as a recommended score value of the current schemes to be evaluated until the traversal of the schemes to be evaluated is completed.
When each recommended score value is obtained, the scheme to be evaluated corresponding to each target label information may be traversed first, and a target library of the currently traversed current scheme to be evaluated in the knowledge graph is determined. The target library can be any one of a public knowledge library, a BUG experience library and a specific service scene library in the knowledge map. And a default recommendation score value is set for each library in the knowledge-graph. Therefore, after the target library is determined, a default score value (i.e., a default recommended score value) of the target library can be obtained, and the default score value is used as the recommended score value of the current scheme to be evaluated. And until all the schemes to be evaluated are traversed, determining that each scheme to be evaluated has a recommended score value.
In this embodiment, each scheme to be evaluated is traversed, a target library placed in the knowledge graph and currently to be evaluated is determined, and a default score value of the target library is used as a recommended score value of the current scheme to be evaluated until the traversal of each scheme to be evaluated is completed. Therefore, the accuracy of the obtained recommended point value of the scheme to be evaluated is guaranteed.
Further, the step of obtaining an evaluation index corresponding to each piece of target tag information includes:
and f, sequentially traversing each piece of target label information, determining a plurality of different types of index quantity values corresponding to the currently traversed current target label information, and taking each index quantity value as an evaluation index corresponding to the current target label information until each piece of target label information is traversed.
In this embodiment, when obtaining the evaluation index, it is necessary to sequentially traverse each piece of target tag information, determine the current traversed target tag information, determine a plurality of different types of index quantity values corresponding to the current target tag information (for example, a case-based library, a service scene, a Test expert experience score, a ranking name of actual use times of a Tctp Test platform, and the like), and use these index quantity values together as the evaluation index corresponding to the current target tag information until the traversal of each piece of target tag information is completed, that is, obtain the evaluation index corresponding to each piece of target tag information.
In this embodiment, the accuracy of the obtained evaluation index is ensured by sequentially traversing each piece of target tag information and taking a plurality of index quantity values corresponding to the currently traversed current target tag information as the evaluation index corresponding to the current target tag information until each piece of target tag information is traversed.
Further, the step of obtaining the index influence factor corresponding to each evaluation index includes:
and h, traversing each evaluation index in sequence, determining all index quantity values in the currently traversed evaluation index, and determining an index influence factor corresponding to the currently evaluated index according to each index quantity value until each evaluation index is traversed.
After the evaluation indexes corresponding to the target label information are obtained, traversing each evaluation index in sequence, determining all index quantity values carried in the currently traversed current evaluation index, and determining the index influence factor corresponding to the current evaluation index according to the importance degree of each index quantity value to the current evaluation index. Until each evaluation index is traversed.
In this embodiment, the index impact factors are determined by traversing each evaluation index and determining all index values in the current evaluation index until each evaluation index is traversed, so that the accuracy of the obtained index impact factors is ensured.
Further, the step of obtaining a first preset number of target label information from untrained label information of the knowledge graph according to the data input source includes:
step m, acquiring a plurality of untrained label information in a knowledge graph, and sequencing the untrained label information according to the data input source;
in this embodiment, training data (about M × 100) with flag being (untrained) needs to be taken near M times in the knowledge graph, training case label information (i.e., untrained label information) under the service scene type is obtained, and each piece of untrained label information is ranked according to the number of times of using the parent function type and the child function type in the data input source as a keyword.
And n, acquiring a first preset number of target label information in each untrained label information according to the sorted sorting result.
And acquiring target label information with a first preset number and a plurality of times of use from each untrained label information according to the sequencing result.
In this embodiment, the plurality of untrained tag information in the knowledge graph are sorted according to the data input source, and the first preset number of target tag information is obtained according to the sorting result, so that the accuracy of the obtained target tag information is ensured.
The present invention also provides a test apparatus, referring to fig. 3, the test apparatus including:
the determining module A10 is configured to determine a data input source according to an input service scenario type, and obtain a first preset number of target label information from untrained label information of a knowledge graph according to the data input source;
an obtaining module a20, configured to obtain an evaluation index corresponding to each piece of target tag information, and obtain an index influence factor corresponding to each evaluation index;
a correcting module a30, configured to calculate a result weight value corresponding to each piece of target label information according to each evaluation indicator and each indicator influence factor, and correct a recommendation score value corresponding to each piece of target label information according to each result weight value;
and the test module A40 is configured to determine a second preset number of target recommendation score values based on the corrected recommendation score values, and test a to-be-evaluated scheme corresponding to each target recommendation score value as a test case.
Optionally, the correction module a30 is further configured to:
calculating a total value of a correction index of the scheme to be evaluated corresponding to each target label information according to each evaluation index and each index influence factor;
and calculating a result weight value corresponding to each target label information according to each correction index total value and each evaluation index.
Optionally, the correction module a30 is further configured to:
acquiring recommendation score values of the to-be-evaluated scheme corresponding to the target label information, sequentially traversing the recommendation score values, and determining a current result weight value corresponding to the currently traversed current recommendation score value in the result weight values;
and calculating the current recommendation score value and the current result weight value based on a preset calculation formula, and correcting the current recommendation score value according to a calculation result until all the recommendation score values are traversed.
Optionally, the correction module a30 is further configured to:
and traversing the schemes to be evaluated corresponding to the target label information in sequence, determining a target library of the currently traversed scheme to be evaluated in the knowledge graph, acquiring a default score value of the target library, and taking the default score value as a recommended score value of the current scheme to be evaluated until the traversal of each scheme to be evaluated is completed.
Optionally, the obtaining module a20 is further configured to:
and traversing all the target label information in sequence, determining a plurality of different types of index quantity values corresponding to the currently traversed current target label information, and taking all the index quantity values as evaluation indexes corresponding to the current target label information until all the target label information is traversed.
Optionally, the obtaining module a20 is further configured to:
and traversing each evaluation index in sequence, determining all index quantity values in the currently traversed evaluation index, and determining an index influence factor corresponding to the currently evaluated index according to each index quantity value until each evaluation index is traversed.
Optionally, the determining module a10 is further configured to:
acquiring a plurality of untrained label information in a knowledge graph, and sequencing the untrained label information according to the data input source;
and acquiring a first preset number of target label information in each untrained label information according to the sequencing result of the sequencing.
The method executed by each program unit can refer to each embodiment of the testing method of the present invention, and is not described herein again.
The invention also provides a computer storage medium.
The computer storage medium of the present invention has stored thereon a test program which, when executed by a processor, implements the steps of the test method described above.
The method implemented when the test program running on the processor is executed may refer to each embodiment of the test method of the present invention, and details are not described here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A method of testing, comprising the steps of:
determining a data input source according to an input service scene type, and acquiring a first preset number of target label information in untrained label information of a knowledge graph according to the data input source;
obtaining evaluation indexes corresponding to the target label information, and obtaining index influence factors corresponding to the evaluation indexes;
calculating a result weight value corresponding to each target label information according to each evaluation index and each index influence factor, and correcting a recommendation score value corresponding to each target label information according to each result weight value;
and determining a second preset number of target recommendation score values based on the corrected recommendation score values, and testing the scheme to be evaluated corresponding to the target recommendation score values as a test case.
2. The method according to claim 1, wherein the step of calculating a result weight value corresponding to each target label information according to each of the evaluation indicators and each of the indicator influence factors includes:
calculating a total value of a correction index of the scheme to be evaluated corresponding to each target label information according to each evaluation index and each index influence factor;
and calculating a result weight value corresponding to each target label information according to each correction index total value and each evaluation index.
3. The testing method according to claim 1, wherein the step of correcting the recommended score value of the solution to be evaluated corresponding to each target tag information according to each result weight value includes:
acquiring recommendation score values of the to-be-evaluated scheme corresponding to the target label information, sequentially traversing the recommendation score values, and determining a current result weight value corresponding to the currently traversed current recommendation score value in the result weight values;
and calculating the current recommendation score value and the current result weight value based on a preset calculation formula, and correcting the current recommendation score value according to a calculation result until all the recommendation score values are traversed.
4. The testing method according to claim 3, wherein the step of obtaining the recommended score value of the to-be-evaluated scheme corresponding to each piece of the target tag information includes:
and traversing the schemes to be evaluated corresponding to the target label information in sequence, determining a target library of the currently traversed scheme to be evaluated in the knowledge graph, acquiring a default score value of the target library, and taking the default score value as a recommended score value of the current scheme to be evaluated until the traversal of each scheme to be evaluated is completed.
5. The testing method according to claim 1, wherein the step of obtaining the evaluation index corresponding to each piece of target tag information includes:
and traversing all the target label information in sequence, determining a plurality of different types of index quantity values corresponding to the currently traversed current target label information, and taking all the index quantity values as evaluation indexes corresponding to the current target label information until all the target label information is traversed.
6. The test method according to claim 1, wherein the step of obtaining the index influence factor corresponding to each of the evaluation indexes comprises:
and traversing each evaluation index in sequence, determining all index quantity values in the currently traversed evaluation index, and determining an index influence factor corresponding to the currently evaluated index according to each index quantity value until each evaluation index is traversed.
7. The test method of any one of claims 1-6, wherein the step of obtaining a first preset amount of target label information from untrained label information of a knowledge-graph according to the data input source comprises:
acquiring a plurality of untrained label information in a knowledge graph, and sequencing the untrained label information according to the data input source;
and acquiring a first preset number of target label information in each untrained label information according to the sequencing result of the sequencing.
8. A test apparatus, characterized in that the test apparatus comprises:
the system comprises a determining module, a judging module and a judging module, wherein the determining module is used for determining a data input source according to an input service scene type and acquiring a first preset amount of target label information in untrained label information of a knowledge graph according to the data input source;
the acquisition module is used for acquiring evaluation indexes corresponding to the target label information and acquiring index influence factors corresponding to the evaluation indexes;
the correction module is used for calculating a result weight value corresponding to each target label information according to each evaluation index and each index influence factor and correcting a recommendation score value corresponding to each target label information according to each result weight value;
and the test module is used for determining a second preset number of target recommendation score values based on the corrected recommendation score values and testing the scheme to be evaluated corresponding to the target recommendation score values as a test case.
9. A test apparatus, characterized in that the test apparatus comprises: memory, a processor and a test program stored on the memory and executable on the processor, the test program, when executed by the processor, implementing the steps of the test method according to any one of claims 1 to 7.
10. A computer storage medium, characterized in that the computer storage medium has stored thereon a test program which, when executed by a processor, implements the steps of the test method according to any one of claims 1 to 7.
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